Top Machine Learning Jobs

Develop and optimize machine learning software for embedded edge devices, integrating inference runtimes, AI accelerators, firmware, drivers, sensors, and host applications. Convert models into production-ready implementations optimized for latency, memory, power, and throughput. Responsibilities include quantization, pruning, benchmarking, profiling, debugging, validation, documentation, and cross-functional platform integration across PCs and connected devices.

Lead architecture and hands-on development of machine learning and data infrastructure for risk and fraud prevention. Optimize model inference performance, build model deployment and monitoring systems, design scalable Databricks and Spark data pipelines, connect offline and online feature systems, and improve feature services and data integrations. Provide technical direction, mentor engineers, and coordinate delivery across ML, data engineering, cloud infrastructure, security, and product teams.

Develops, integrates, tests, and deploys autonomy, artificial intelligence, and machine learning algorithms for mission systems. Responsibilities include perception, planning, controls, model training, software development, requirements translation, human-machine interfaces, troubleshooting, debugging, and lifecycle support. The role requires applied machine learning experience, advanced education or equivalent experience, and proficiency with AI/ML frameworks and cloud tools.

Build and maintain large-scale machine learning infrastructure, data pipelines, analytics platforms, and distributed systems in AWS. Develop and scale model training and deployment frameworks, implement ETL and real-time data processing, and create CI/CD and observability solutions. Research reinforcement learning algorithms, including Monte Carlo Tree Search, and validate multi-agent models using simulation platforms. Partner with engineering and business teams, advise management on technical strategy, coordinate complex projects, and mentor junior engineers.

Leads the Applied AI Mission Integration team and STAR.Data product, overseeing product development, deployment, mission-system integration, and trusted AI solutions across Lockheed Martin business areas and external customers. The role manages high-performing technical teams, coordinates technology transitions, cultivates talent, and advances data-sharing capabilities for contested and bandwidth-constrained environments.

Develops production-grade AI and machine-learning solutions, including LLM integrations, predictive models, autonomy algorithms, and end-to-end pipelines. Translates business and mission requirements into solution architectures, builds and tests models, integrates APIs, deploys through CI/CD, and monitors performance. Collaborates with stakeholders, documents designs, supports user adoption, troubleshoots issues, and drives continuous improvement. The role also involves deep learning, generative AI, MLOps, knowledge graphs, cloud platforms, and research into emerging technologies.

Develops, integrates, tests, and deploys AI and machine learning algorithms for data engineering platforms. Builds intelligent orchestration, data quality validation, metadata discovery, anomaly detection, predictive operations, and automation capabilities. Integrates ML into ETL/ELT workflows and cloud data infrastructure, troubleshoots production systems, and creates interfaces enabling data teams to use AI tools. Requires expert Python, production ML, MLOps, cloud, data engineering, and leadership expertise.

Develops production-grade AI and machine-learning solutions, including LLM integrations, data pipelines, autonomy algorithms, testing, deployment, monitoring, and continuous improvement. Translates business and mission requirements into AI architectures, integrates models into applications, and collaborates with stakeholders to deliver measurable value. Requires Python, deep-learning, generative AI, REST API, and software-development expertise, with preferred experience in MLOps, cloud platforms, knowledge graphs, containers, and Git.

Develop and deploy machine learning and autonomy algorithms for radar tracking systems. Research advanced ML applications, build and integrate tracking capabilities, assess model performance using customer data, and support design, testing, troubleshooting, and fielding. Collaborate with cross-functional teams to address operational requirements while contributing across the software development lifecycle. The role is on-site in Moorestown, New Jersey and requires U.S. citizenship.

Develops and deploys production-grade agentic AI systems, including tool-calling, RAG pipelines, and autonomous workflows. Builds platform features, test automation, security enhancements, and GitLab CI/CD pipelines for containerized services. Implements vulnerability remediation, authentication, compliance artifacts, observability, and AI evaluation practices. Requires strong Python engineering, production GenAI experience, cloud and DevSecOps skills, and collaboration across the platform. US citizenship is required for program access.

Develops, integrates, tests, and deploys AI, machine learning, autonomy, perception, planning, controls, and agentic automation algorithms for mission systems. Translates requirements into software, supports the full development lifecycle, troubleshoots and debugs implementations, and develops human-machine interfaces. The role also manages automation projects, process mapping, ontology engineering, RPA solutions, CI/CD workflows, and stakeholder requirements.

Develops data pipelines and AI adoption analytics infrastructure for enterprise-wide AI transformation. Builds ETL/ELT workflows, AI/ML data models, quality controls, governance processes, and dashboards using Python, SQL, and orchestration tools. Integrates data from enterprise and AI platforms, supports secure on-premises and classified environments, and translates adoption metrics into executive reporting. Collaborates across engineering, product, cybersecurity, and leadership teams while contributing reusable components and mentoring junior staff.

Develops and deploys AI, machine learning, autonomy, and generative-AI solutions for mission systems and enterprise applications. Builds end-to-end pipelines, integrates LLM capabilities, implements perception and planning algorithms, and supports requirements, software development, testing, troubleshooting, and deployment. Collaborates with stakeholders, researches emerging technologies, and contributes to human-machine interfaces, MLOps, knowledge graphs, and cloud-based AI solutions.

Develops production-grade AI and machine-learning solutions, including LLM integrations, end-to-end pipelines, autonomy algorithms, testing, deployment, monitoring, and continuous improvement. Translates business and mission requirements into solution architectures, integrates AI capabilities into applications, and collaborates with stakeholders on adoption and innovation. Requires Python, machine-learning lifecycle knowledge, generative AI experience, REST API integration, and familiarity with MLOps, cloud, containerization, and knowledge-graph technologies.

Leads and executes software-intensive programs focused on AI-enabled mission applications, autonomy, command and control, and tactical systems. Integrates advanced technologies across multidisciplinary teams, develops software in C++, Java, and Python, and uses CI/CD, containers, Linux, and agile tools. Applies AI/ML optimization techniques, supports safety-critical solutions, develops technical proposals, and leads teams toward mission-critical objectives.

Develops, integrates, tests, and deploys AI, machine learning, autonomy, perception, planning, and control algorithms for mission systems. Translates requirements into production-grade machine-learning pipelines and software, integrates generative AI and LLM capabilities, troubleshoots implementations, and supports research-driven innovation. Collaborates with stakeholders, documents interfaces, develops human-machine interaction scenarios, and advances enterprise AI adoption.

Develops, integrates, tests, and deploys AI, machine learning, autonomy, perception, planning, controls, and human-machine interface capabilities for mission systems. Translates requirements into production software, builds LLM and generative AI systems with agentic workflows, troubleshoots integrations, and supports the full development lifecycle. The role requires Python, React, TypeScript, cloud platforms, containerization, and software engineering practices.

Develops, integrates, tests, and deploys autonomy, AI, machine learning, and agentic automation algorithms for mission systems. Translates requirements into software, supports perception, planning, controls, human-machine interfaces, and troubleshooting throughout the development lifecycle. Builds RPA and LLM-driven workflows using Python, automation frameworks, graph databases, ontology engineering, and CI/CD practices. Manages automation projects, documents technical solutions, and collaborates with stakeholders to deliver scalable solutions.

Develops, integrates, tests, and deploys autonomy, artificial intelligence, and machine learning algorithms for mission systems. Responsibilities include requirements translation, software design, model training and evaluation, perception and planning functionality, human-machine interfaces, troubleshooting, debugging, and lifecycle support. The role requires advanced technical expertise, software engineering capabilities, and collaboration across development and integration activities.

Leads the design, development, deployment, and support of enterprise machine learning and generative AI systems. Builds production solutions using LLMs, RAG, agentic frameworks, model orchestration, and ML platforms. Applies advanced mathematics, model governance, risk management, responsible AI, monitoring, and regulatory controls. Collaborates with internal clients and technical teams, manages requirements and releases, and supports production models through testing, documentation, and continuous improvement.

Build and own production machine learning systems for sensitive enterprise data, including RAG pipelines, fine-tuned language models, multi-agent systems, evaluation frameworks, and private inference infrastructure. Ensure model reliability, accuracy, observability, auditability, and secure integration within controlled environments. Collaborate with platform and infrastructure teams to scale AI capabilities for high-stakes customers.

Leads hardware/software co-design for AI silicon by translating ML workload requirements into kernel, compiler, and architectural decisions. Optimizes ML kernels, quantization, data structures, memory usage, and execution efficiency; develops performance models and simulations; defines specialized acceleration IP; and validates silicon requirements with ML research, compiler, and architecture teams.

Design, develop, and operate scalable VoIP infrastructure and telephony platforms supporting AI-driven voice workloads. Integrate SIP/RTP, Kamailio, RTPEngine, PSTN systems, LLMs, and real-time communication technologies. Build reliable cloud-native distributed systems on Kubernetes, improve SRE and observability practices, automate deployments, develop software tooling, and maintain high-quality code. Collaborate across engineering, product, infrastructure, and AI teams while mentoring colleagues.

Develop and improve machine learning models for robotic manipulation, including VLA and world-action models. Build robot-learning data pipelines, conduct simulation and real-robot evaluations, and deploy optimized models for real-time onboard inference. The role involves multimodal learning, reinforcement learning, tactile and force-torque sensing, and close collaboration with robotics, hardware, and product teams. This is a full-time, 3–6 month internship requiring five days per week in the office.

Leads the design, development, and scaling of machine learning systems for signals, attribution, recommendation, ranking, and advertising measurement. Provides technical direction, improves ML infrastructure, collaborates with cross-functional leadership, and promotes scalable, reliable, cost-effective engineering practices. The role requires shipping advanced models, mentoring engineers, solving ambiguous technical problems, and influencing Snap’s broader machine learning strategy.